Industry 4.0
At Altis, we know that data is at the core of the 4th Industrial Revolution (Industry 4.0).
How we help
With more than 20 years’ experience in Data and Analytics, we know how to help organizations embrace Industry 4.0 by releasing the power of their data.
Altis and Industry 4.0
Industry 4.0 relies on interfaces to capture and present data at different stages of the process. The data captured can be:
- Unstructured, such as image, audio or video content.
- Semi-structured proceeding from API calls for example.
- Structured, typically records to or from a relational database.
Interfaces are either machine-to-machine (no human involved) or machine-to-human where the producer or consumer of the data is a human. They can include:
- IOT devices (Machine-to-machine)
- Wearable Health monitors (Human-to-machine)
- Screens (Machine-to-human)
The principal characteristic of Industry 4.0 in comparison to traditional Data and Analytics is that the result of a data process (e.g. a Machine Learning algorithm) is not presented to human being, but rather used by another automated process.
The decision making automation and the following action is the main paradigm shift introduced by Industry 4.0.
As suggested above, advanced processing is often applied to captured data before it can be used. This is where concepts of Artificial Intelligence, Machine Learning and Analytics are applied.
The applications are countless from manufacturing to asset management, from healthcare to online retail.
Examples
In the utilities sector, Altis has been working with clients on IoT and advanced analytics initiatives which automate the collection, processing and analysis of data as well as the automatic triggering of actions in operational systems. Examples include:
- Interfacing with CRM systems to send automated SMS notifications to customers based on analysis of data processed from digital meters such as water continuous flow notifications, loss of electricity supply or unusually high water / electricity usage
- Creating asset maintenance jobs in works management systems based on the outputs from predictive machine learning models
